#Classifier based on attributes independence assumption

#given training and testsets reduced to meaningful columns: [ classNo, attr, attr, ...., attr ]
#result matrix contains two rows: 1st: occurencies of each class in the testset, 2nd: classification errors within each class
#
#train - matrix of samples(rows) and their relevant attributes (cols) prepended by class id (1st col)
#test - same as above 
function results=coreTask1(train, test, apriori=[0.25,0.25,0.25,0.25])
    results=[];
    M = getMeans(train);
    S = getStdevs(train);
    results = performTest(test,M,S,apriori);    
end;
